In recent years, run-by run (RbR) control mechanism has emerged as an useful tool for keeping complex semiconductor manufacturing processes on target during repeated short production runs. Many types of RbR controllers exist in the literature of which the exponentially weighted moving average (EWMA) controller is widely used in the industry. However, EWMA controllers are known to have several limitations. For example, in the presence of multiscale disturbances and lack of accurate process models, the performance of EWMA controller deteriorates and often fails to control the process. Also control of complex manufacturing processes requires sensing of multiple parameters that may be spatially distributed. New control strategies that can successfully use spatially distributed sensor data are required.
Run-by-Run (RbR) process control is a combination of Statistical Process Control (SPC) and Engineering Process Control (EPC). The set points of the automatic PID controllers, which control a process during a run, generally change from one run to the other to account for process disturbances. RbR controllers perform the critical function of obtaining the set point for each new run. The design of a RbR control system primarily consists of two steps—process modeling, and online model tuning and control. Process modeling is done offline using techniques like response surface methods and ordinary least squares estimation. Online model tuning and control is achieved by the combination of offset prediction using a filter, and recipe generation based on a process model (control law). This approach to RbR process control has many limitations that need to be addressed in order to increase its viability to distributed sensing environments. For example, many process controllers rely on good process models that are seldom available for large scale nonlinear systems made up of many interacting subsystems. Even when good (often complex) models are available, the issue becomes the speed of execution of the control algorithms during online applications, which ultimately forces model simplification and resultant suboptimal control. Also the processes are often plagued with multiscale (multiple freq.) noise, which, if not precisely removed, leads to serious lack of controller efficiency.